Search results for "Intelligence"
showing 10 items of 6959 documents
Multimodal Images Classification using Dense SURF, Spectral Information and Support Vector Machine
2019
International audience; The multimodal image classification is a challenging area of image processing which can be used to examine the wall painting in the cultural heritage domain. In such classification, a common space of representation is important. In this paper, we present a new method for multimodal representation learning, by using a pixel-wise feature descriptor named dense Speed Up Robust Features (SURF) combined with the spectral information carried by the pixel. For classification of extracted features we have used support vector machine (SVM). Our database was extracted from acquisition on cultural heritage wall paintings that contain four modalities UV, Visible, IRR and fluores…
Iteratively Learning a Liver Segmentation Using Probabilistic Atlases: Preliminary Results
2016
This works deals with the concept of liver segmentation by using a priori information based on probabilistic atlases and segmentation learning based of previous steps. A probabilistic atlas is here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedure to segment Perfusion Magnetic Resonance liver images that combines both: a probabilistic atlas of the liver and a segmentation algorithm based on global information of previous simpler segmentation steps, local information from close segmented slices and finally a mathematical morphology procedure, namely viscous reconstruction, to…
A new image segmentation approach using community detection algorithms
2015
Image segmentation has an important role in many image processing applications. Several methods exist for segmenting an image. However, this technique is still a relatively open topic for which various research works are regularly presented. With the recent developments on complex networks theory, image segmentation techniques based on graphs has considerably improved. In this paper, we present a new perspective of image segmentation, by applying three of the most efficient community detection algorithms, Louvain, infomap and stability optimization based on the louvain algorithm, and we extract communities in which the highest modularity feature is achieved. After we show that this measure …
Automatic skull stripping in MRI based on morphological filters and fuzzy c-means segmentation
2012
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into account the result obtained from the preceding slice to solve the organ splitting problem. The proposed approach is compared with BET (Brain Extraction Tool) implemented in MRIcro software.
Synchronizing eye tracking and optical motion capture : How to bring them together
2018
Both eye tracking and motion capture technologies are nowadays frequently used in human sciences, although both technologies are usually used separately. However, measuring both eye and body movements simultaneously would offer great potential for investigating cross- modal interaction in human (e.g. music and language-related) behavior. Here we combined an Ergoneers Dikablis head mounted eye tracker with a Qualisys Oqus optical motion cap- ture system. In order to synchronize the recordings of both devices, we developed a gener- alizable solution that does not rely on any (cost-intensive) ready-made / company-provided synchronization solution. At the beginning of each recording, the partic…
Improving Lossless Image Compression with Contextual Memory
2019
With the increased use of image acquisition devices, including cameras and medical imaging instruments, the amount of information ready for long term storage is also growing. In this paper we give a detailed description of the state-of-the-art lossless compression software PAQ8PX applied to grayscale image compression. We propose a new online learning algorithm for predicting the probability of bits from a stream. We then proceed to integrate the algorithm into PAQ8PX&rsquo
A Pragmatic Characterization of Concept Algebra
2017
Taking into account the framework of denotational mathematics as seen by Yingxu Wang, in this paper the author wishes to implement a possible further pragmatic (context-depend) dimension into the algebraic structure of concept algebra. One of the main problems of software science is that regarding context-depend question of a programming language. Indeed, attention has been paid above all to syntactic and semantic dimensions of a programming language, neglecting the pragmatic one concerning context. The author has tried to face this question providing a first denotational mathematics structure taking into account a possible pragmatic dimension.
Fuzzy Logic Based Security Trust Evaluation for IoT Environments
2019
In the new technological era called the Internet of Things (IoT), people, machines and objects communicate with each other via the Internet by exchanging information. In this context, trust plays an important role and is considered as a key factor in the success of the IoT services expansion. IoT services and applications use in some cases data concerning the privacy of their users. Consequently, users should trust the entities exchanging their personal information. In this paper, we present a framework that evaluates the security trust level of IoT nodes based on a Fuzzy Logic model using different input parameters such as Device Physical Security, Device Security Level and Device Ownershi…
Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting.
2015
De novo clustering is a popular technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we introduce a new dendrogram-based OTU clustering pipeline called CRiSPy. The key idea used in CRiSPy to improve clustering accuracy is the application of an anomaly detection technique to obtain a dynamic distance cutoff instead of using the de facto value of 97 percent sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt change in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clusterin…
The impact of sample reduction on PCA-based feature extraction for supervised learning
2006
"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Naive Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PC…